Across many industries, it is useful to study the motion of a moving target, and to use a resultant analysis of the motion to improve performance of the moving target. In the case where a human being is a selected moving target, for example, the fitness industry and the health industry (e.g., physical therapy and occupational therapy) may find the study of certain human motions beneficial and advantageous.
Various techniques are known for sensing motion in real time, including using a variety of sensors that can be affiliated with the moving target. Some exemplary sensors include accelerometers, gyroscopes, and other devices. A moving target may be any of a number of objects. For example, a moving target may be all or part of an animal, including a human, a horse, a dog, a bird, or a fish or other animals. A moving target may be all or part of a vehicle, including an automobile, aircraft, spacecraft, ships, trucks, recreational vehicles, and more. A moving target may also be a robotic apparatus, or machine componentry with moving parts. A moving target can even be a missile.
Certain existing motion sensing and processing systems provide users a way to analyze data by comparing real-time data to statistical models representing certain types of known motions. Some such systems additionally include machine learning algorithms, capable of updating the statistical models based on real-time sensed motion data. Unfortunately, systems relying on statistical models in lieu of actual data for comparison purposes lose information about the data by reliance on statistics. That loss can limit what can be compared and what can be analyzed, especially in the quantification of real-time motion data. Certain existing systems for motion analysis also lack accuracy due at least in part by reliance on statistics rather than reliance on actual data for comparison.
Accurate and efficient systems, devices and methods for analyzing motion, in terms of qualification and quantification, are needed.